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refactoring allen smFish with new spot finding
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Shannon Axelrod
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Oct 11, 2019
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183
starfish/core/spots/FindSpots/trackpy_local_max_peak_finder.py
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Original file line number | Diff line number | Diff line change |
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import warnings | ||
from functools import partial | ||
from typing import Optional, Tuple, Union | ||
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import numpy as np | ||
import xarray as xr | ||
from trackpy import locate | ||
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from starfish.core.image.Filter.util import determine_axes_to_group_by | ||
from starfish.core.imagestack.imagestack import ImageStack | ||
from starfish.core.spots.FindSpots import spot_finding_utils | ||
from starfish.core.types import Axes, SpotAttributes, SpotFindingResults | ||
from ._base import FindSpotsAlgorithm | ||
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class TrackpyLocalMaxPeakFinder(FindSpotsAlgorithm): | ||
""" | ||
Find spots using a local max peak finding algorithm | ||
This is a wrapper for :code:`trackpy.locate`, which implements a version of the | ||
`Crocker-Grier <crocker_grier>`_ algorithm. | ||
.. _crocker_grier: https://physics.nyu.edu/grierlab/methods3c/ | ||
Parameters | ||
---------- | ||
spot_diameter : | ||
odd integer or tuple of odd integers. | ||
This may be a single number or a tuple giving the feature’s extent in each dimension, | ||
useful when the dimensions do not have equal resolution (e.g. confocal microscopy). | ||
The tuple order is the same as the image shape, conventionally (z, y, x) or (y, x). | ||
The number(s) must be odd integers. When in doubt, round up. | ||
min_mass : Optional[float] | ||
The minimum integrated brightness. This is a crucial parameter for eliminating spurious | ||
features. Recommended minimum values are 100 for integer images and 1 for float images. | ||
Defaults to 0 (no filtering). | ||
max_size : float | ||
maximum radius-of-gyration of brightness, default None | ||
separation : Union[float, tuple] | ||
Minimum separation between features. Default is diameter + 1. May be a tuple, see | ||
diameter for details. | ||
percentile : float | ||
Features must have a peak brighter than pixels in this percentile. This helps eliminate | ||
spurious peaks. (default = 0) | ||
noise_size : Union[float, tuple] | ||
Width of Gaussian blurring kernel, in pixels Default is 1. May be a tuple, see diameter | ||
for details. | ||
smoothing_size : Union[float, tuple] | ||
The size of the sides of the square kernel used in boxcar (rolling average) smoothing, | ||
in pixels Default is diameter. May be a tuple, making the kernel rectangular. | ||
threshold : float | ||
Clip bandpass result below this value. Thresholding is done on the already | ||
background-subtracted image. By default, 1 for integer images and 1/255 for float | ||
images. | ||
measurement_type : str ['max', 'mean'] | ||
name of the function used to calculate the intensity for each identified spot area | ||
(default = max) | ||
preprocess : boolean | ||
Set to False to turn off bandpass prepossessing. | ||
max_iterations : integer | ||
Max number of loops to refine the center of mass, (default = 10) | ||
is_volume : bool | ||
if True, run the algorithm on 3d volumes of the provided stack (default = False) | ||
verbose : bool | ||
If True, report the percentage completed (default = False) during processing | ||
Notes | ||
----- | ||
See also trackpy locate: http://soft-matter.github.io/trackpy/dev/generated/trackpy.locate.html | ||
""" | ||
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def __init__( | ||
self, spot_diameter, min_mass, max_size, separation, percentile=0, | ||
noise_size: Tuple[int, int, int] = (1, 1, 1), smoothing_size=None, threshold=None, | ||
preprocess: bool = False, max_iterations: int = 10, measurement_type: str = 'max', | ||
is_volume: bool = False, verbose=False, radius_is_gyration: bool = False) -> None: | ||
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self.diameter = spot_diameter | ||
self.minmass = min_mass | ||
self.maxsize = max_size | ||
self.separation = separation | ||
self.noise_size = noise_size | ||
self.smoothing_size = smoothing_size | ||
self.percentile = percentile | ||
self.threshold = threshold | ||
self.measurement_function = self._get_measurement_function(measurement_type) | ||
self.preprocess = preprocess | ||
self.max_iterations = max_iterations | ||
self.is_volume = is_volume | ||
self.verbose = verbose | ||
self.radius_is_gyration = radius_is_gyration | ||
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def image_to_spots(self, data_image: Union[np.ndarray, xr.DataArray]) -> SpotAttributes: | ||
""" | ||
Parameters | ||
---------- | ||
data_image : np.ndarray | ||
three-dimensional image containing spots to be detected | ||
Returns | ||
------- | ||
SpotAttributes : | ||
spot attributes table for all detected spots | ||
""" | ||
data_image = np.asarray(data_image) | ||
with warnings.catch_warnings(): | ||
warnings.simplefilter('ignore', FutureWarning) # trackpy numpy indexing warning | ||
warnings.simplefilter('ignore', UserWarning) # yielded if black images | ||
attributes = locate( | ||
data_image, | ||
diameter=self.diameter, | ||
minmass=self.minmass, | ||
maxsize=self.maxsize, | ||
separation=self.separation, | ||
noise_size=self.noise_size, | ||
smoothing_size=self.smoothing_size, | ||
threshold=self.threshold, | ||
percentile=self.percentile, | ||
preprocess=self.preprocess, | ||
max_iterations=self.max_iterations, | ||
) | ||
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# when zero spots are detected, 'ep' is missing from the trackpy locate results. | ||
if attributes.shape[0] == 0: | ||
attributes['ep'] = [] | ||
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# TODO ambrosejcarr: data should always be at least pseudo-3d, this may not be necessary | ||
# TODO ambrosejcarr: this is where max vs. sum vs. mean would be parametrized. | ||
# here, total_intensity = sum, intensity = max | ||
new_colnames = [ | ||
'y', 'x', 'total_intensity', 'radius', 'eccentricity', 'intensity', 'raw_mass', 'ep' | ||
] | ||
if len(data_image.shape) == 3: | ||
attributes.columns = ['z'] + new_colnames | ||
else: | ||
attributes.columns = new_colnames | ||
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attributes['spot_id'] = np.arange(attributes.shape[0]) | ||
return SpotAttributes(attributes) | ||
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def run( | ||
self, | ||
image_stack: ImageStack, | ||
reference_image: Optional[ImageStack] = None, | ||
n_processes: Optional[int] = None, | ||
*args, | ||
) -> SpotFindingResults: | ||
""" | ||
Find spots. | ||
Parameters | ||
---------- | ||
image_stack : ImageStack | ||
ImageStack where we find the spots in. | ||
reference_image : xr.DataArray | ||
(Optional) a reference image. If provided, spots will be found in this image, and then | ||
the locations that correspond to these spots will be measured across each channel. | ||
n_processes : Optional[int] = None, | ||
Number of processes to devote to spot finding. | ||
""" | ||
spot_finding_method = partial(self.image_to_spots, *args) | ||
if reference_image: | ||
data_image = reference_image._squeezed_numpy(*{Axes.ROUND, Axes.CH}) | ||
reference_spots = spot_finding_method(data_image) | ||
results = spot_finding_utils.measure_intensities_at_spot_locations_across_imagestack( | ||
image_stack, | ||
reference_spots, | ||
measurement_function=self.measurement_function, | ||
radius_is_gyration=self.radius_is_gyration) | ||
else: | ||
spot_attributes_list = image_stack.transform( | ||
func=spot_finding_method, | ||
group_by=determine_axes_to_group_by(self.is_volume), | ||
n_processes=n_processes | ||
) | ||
results = SpotFindingResults(imagestack_coords=image_stack.xarray.coords, | ||
log=image_stack.log, | ||
spot_attributes_list=spot_attributes_list) | ||
return results |
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